Bayesian Logistic Regression with Heavy-Tailed Priors
Create a Matrix of Markov Chain Samples
Bias-corrected Bayesian classification initial state
Evaluate Prediction Results
Generate Simulated Data with Factor Analysis Model
Generate Simulated Data with Multinomial Logistic Regression Model
Bayesian Logistic Regression with Heavy-Tailed Priors
Fit a HTLR Model
Fit a HTLR Model (Internal API)
Make Prediction on New Data (Advanced)
Generate Prior Configuration
Lasso Initial State
Get Indices of Non-Zero Coefficients
Order features by F-statistic
Order features by Kruskal-Wallis test
Plain order function
Pipe operator
Make Prediction on New Data
Split Data into Train and Test Partitions
Standardizes a Design Matrix
Posterior Summaries
Efficient Bayesian multinomial logistic regression based on heavy-tailed (hyper-LASSO, non-convex) priors. The posterior of coefficients and hyper-parameters is sampled with restricted Gibbs sampling for leveraging the high-dimensionality and Hamiltonian Monte Carlo for handling the high-correlation among coefficients. A detailed description of the method: Li and Yao (2018), Journal of Statistical Computation and Simulation, 88:14, 2827-2851, <arXiv:1405.3319>.